Ballot Measures & Election Results

Official data sources for tracking ballot initiatives, referendums, propositions, and election outcomes. Essential for monitoring water fluoridation votes, school bond measures, health policy propositions, and other civic engagement opportunities.

📊 Data Scale & Coverage

Data Type	Source	Coverage	Cost
Ballot Measures	Ballotpedia	All states, historical	API limited (paid at scale)
Election Results	MIT Election Lab	Presidential/Congressional	Free
Certified Results	OpenElections	State-by-state	Free (CSV)

🗳️ Primary Data Sources

1. Ballotpedia ⭐ Most Comprehensive

Organization: Lucy Burns Institute
URL: https://ballotpedia.org/
API: https://ballotpedia.org/API-documentation

What It Contains:

State ballot measures - Propositions, referendums, constitutional amendments
Local ballot measures - City/county questions, bond issues, tax levies
Initiative campaigns - Signature gathering, qualification status
Election results - Historical outcomes, vote counts, passage status
Full text - Complete measure language and official summaries
Timeline data - Filing dates, election dates, certification dates

Coverage:

✅ All 50 states + DC
✅ Historical data back to 1990s
✅ Local measures in major cities
✅ Water fluoridation votes (highly relevant!)
✅ School bond measures
✅ Tax and spending propositions

API Access:

Free tier: Limited queries (suitable for testing)
Paid tier: Full API access required for production scale
Alternative: Web scraping (permitted with rate limiting)

How We Use It:

# Example: Find fluoridation ballot measures
import requests

# Ballotpedia search (web scraping approach)
def search_fluoridation_measures(state_code, year):
    """Search for water fluoridation ballot measures"""
    base_url = "https://ballotpedia.org"
    search_query = f"{state_code} fluoridation ballot measure {year}"
    # Return measure details
    return {
        'measure_id': 'CA-2024-PROP-15',
        'jurisdiction_id': 'ocd-division/country:us/state:ca',
        'title': 'Water Fluoridation Mandate',
        'election_date': '2024-11-05',
        'result': 'passed',
        'yes_percentage': 67.2,
        'ballotpedia_url': 'https://ballotpedia.org/...'
    }

Data Model Integration:

-- BALLOT_MEASURE entity includes:
ballotpedia_url TEXT  -- Direct link to Ballotpedia page
measure_number TEXT   -- e.g., "Proposition 15", "Question 2"
result TEXT           -- passed, failed, pending
yes_votes INTEGER
no_votes INTEGER
yes_percentage FLOAT

Use Cases:

✅ Track fluoridation votes across all states
✅ Monitor school bond elections (dental program funding)
✅ Alert advocates when measures qualify for ballot
✅ Historical analysis of health policy votes

2. MIT Election Data + Science Lab

Organization: Massachusetts Institute of Technology
URL: https://electionlab.mit.edu/data
Repository: https://github.com/MEDSL/official-returns

What It Contains:

Presidential election results (1976-2020) by state and county
U.S. Senate election results (1976-2020)
U.S. House election results (1976-2020)
Gubernatorial elections (1976-2020)
County-level results - Granular vote totals
Certified official results - From Secretaries of State

Coverage:

✅ Federal elections only (not ballot measures)
✅ All 50 states + DC
✅ County-level granularity
✅ Historical trends (45+ years)
✅ 100% free bulk downloads

Format:

CSV files - Easy to ingest
Standardized schema - Consistent across states
Annual updates - New elections added promptly

How We Use It:

import pandas as pd

# Download county-level presidential results
url = "https://dataverse.harvard.edu/api/access/datafile/4299753"
df = pd.read_csv(url)

# Filter for specific state/county
results = df[
    (df['state'] == 'NORTH CAROLINA') & 
    (df['year'] == 2020)
]

# Join with JURISDICTION data to link elections to jurisdictions
merged = results.merge(
    jurisdictions_df,
    left_on=['state_po', 'county_name'],
    right_on=['state_code', 'county']
)

Data Model Integration:

-- Can link to JURISDICTION for context
-- Useful for understanding political climate in each jurisdiction
CREATE TABLE election_results (
    result_id TEXT PRIMARY KEY,
    jurisdiction_id TEXT REFERENCES jurisdictions(jurisdiction_id),
    election_date DATE,
    office TEXT,  -- President, Senate, House, etc.
    candidate TEXT,
    party TEXT,
    votes INTEGER,
    vote_percentage FLOAT
);

Use Cases:

✅ Understand political composition of jurisdictions
✅ Correlate election outcomes with policy decisions
✅ Identify swing counties for targeted advocacy
✅ Historical context for ballot measure success rates

3. OpenElections ⭐ Free & Certified

Organization: Open Elections Project
URL: https://openelections.net/
GitHub: https://github.com/openelections

What It Contains:

State-by-state certified election results
All offices - Presidential, Congressional, State, Local
All election types - General, Primary, Special, Runoff
Precinct-level data - Highly granular (where available)
Official certified results - Directly from election officials
Standardized CSV format - Easy to parse and analyze

Coverage:

✅ All 50 states (various completion levels)
✅ Presidential elections (nearly complete)
✅ Statewide races (good coverage)
✅ Local races (partial coverage)
⚠️ Ballot measures coverage varies by state

State Coverage Status: See: https://github.com/openelections/openelections-data-ok

Format:

county,precinct,office,district,party,candidate,votes
Wake,01-001,President,,DEM,Joe Biden,1234
Wake,01-001,President,,REP,Donald Trump,987

How We Use It:

import pandas as pd

# Download North Carolina 2020 results
url = "https://raw.githubusercontent.com/openelections/openelections-data-nc/master/2020/20201103__nc__general__precinct.csv"
df = pd.read_csv(url)

# Filter for specific county
wake_results = df[df['county'] == 'Wake']

# Aggregate by jurisdiction
jurisdiction_totals = wake_results.groupby(['office', 'candidate']).agg({
    'votes': 'sum'
}).reset_index()

Data Model Integration:

-- Similar to MIT Election Lab, but more granular
CREATE TABLE precinct_results (
    result_id TEXT PRIMARY KEY,
    jurisdiction_id TEXT REFERENCES jurisdictions(jurisdiction_id),
    precinct_id TEXT,
    election_date DATE,
    office TEXT,
    district TEXT,
    party TEXT,
    candidate TEXT,
    votes INTEGER
);

Use Cases:

✅ Precinct-level advocacy targeting
✅ Voter turnout analysis
✅ Identify competitive jurisdictions
✅ Track local races (school board, city council)

Lakehouse Integration:

Bronze Layer - Raw CSV downloads from GitHub
Silver Layer - Standardized, deduplicated results
Gold Layer - Aggregated to jurisdiction level with OCD-IDs
Join with JURISDICTION - Link elections to government entities

🎯 Fluoridation Vote Tracking (Use Case)

Goal: Track all water fluoridation ballot measures across the United States

Data Sources Combination:

Ballotpedia - Identify fluoridation measures, get full text
OpenElections - Get precinct-level results where available
MIT Election Lab - County-level context for political analysis

Example Query:

# Find all fluoridation votes
fluoridation_measures = ballot_measures_df[
    ballot_measures_df['title'].str.contains('fluorid', case=False, na=False)
]

# Get results
for measure_id in fluoridation_measures['measure_id']:
    results = get_election_results(measure_id)
    # Alert advocates if measure is upcoming
    if results['status'] == 'qualified':
        send_advocacy_alert(measure_id)

📊 Data Availability Summary

Source	Ballot Measures	Election Results	Historical Data	Cost	Format
Ballotpedia	✅ Comprehensive	✅ Yes	✅ 1990s+	💰 API paid	HTML/API
MIT Election Lab	❌ No	✅ Federal only	✅ 1976+	✅ Free	CSV
OpenElections	⚠️ Varies by state	✅ All levels	✅ State-dependent	✅ Free	CSV

Recommendation:

Use Ballotpedia for ballot measure discovery and tracking
Use OpenElections for detailed precinct-level results (free)
Use MIT Election Lab for county-level political context (free)

🔗 Integration with Data Model

BALLOT_MEASURE Entity

Data Sources Referenced:

ballotpedia_url → Ballotpedia
full_text_url → State Secretary of State websites
Election results → OpenElections or MIT Election Lab

🚀 Implementation Roadmap

Phase 1: Ballotpedia Integration (Current Priority)

Create scripts/extract_ballotpedia_measures.py
Web scraping with rate limiting (respect robots.txt)
Search for fluoridation-related measures
Extract measure details and URLs
Save to data/gold/ballots_state_measures.parquet

Phase 2: OpenElections Integration

Create scripts/extract_openelections_results.py
Download state CSV files from GitHub
Standardize to common schema
Link to jurisdictions using OCD-IDs
Save to data/gold/ballots_election_results.parquet

Phase 3: MIT Election Lab Integration

Download county-level presidential results
Join with JURISDICTION data
Calculate political composition metrics
Use for advocacy targeting

Phase 4: Real-time Monitoring

Set up alerts for newly qualified measures
Monitor election certification dates
Update results post-election
Notify advocates of opportunities

📚 References & Credits

Official Sources

Ballotpedia - Lucy Burns Institute, https://ballotpedia.org/
MIT Election Data + Science Lab - https://electionlab.mit.edu/
OpenElections - Open source project, https://openelections.net/

Open Civic Data Standards

OCD Division IDs - https://github.com/opencivicdata/ocd-division-ids
OCDEP 2 Specification - https://open-civic-data.readthedocs.io/en/latest/proposals/0002.html

Data Model ERD - Full entity relationship diagram
Jurisdiction Discovery - How jurisdictions are identified
HuggingFace Datasets - Published datasets catalog

🤝 Contributing

Have a suggestion for another ballot/election data source? Please contribute!

Check if the source is free and public
Verify data quality and official status
Test integration with existing data model
Submit a pull request with documentation

Potential future sources:

State Secretary of State APIs
County election board websites
Voter information portals
Campaign finance databases (for measure funding)

📊 Data Scale & Coverage​

🗳️ Primary Data Sources​

1. Ballotpedia ⭐ Most Comprehensive​

2. MIT Election Data + Science Lab​

3. OpenElections ⭐ Free & Certified​

🎯 Fluoridation Vote Tracking (Use Case)​

📊 Data Availability Summary​

🔗 Integration with Data Model​

BALLOT_MEASURE Entity​

🚀 Implementation Roadmap​

Phase 1: Ballotpedia Integration (Current Priority)​

Phase 2: OpenElections Integration​

Phase 3: MIT Election Lab Integration​

Phase 4: Real-time Monitoring​

📚 References & Credits​

Official Sources​

Open Civic Data Standards​

Related Documentation​

🤝 Contributing​

📊 Data Scale & Coverage

🗳️ Primary Data Sources

1. Ballotpedia ⭐ Most Comprehensive

2. MIT Election Data + Science Lab

3. OpenElections ⭐ Free & Certified

🎯 Fluoridation Vote Tracking (Use Case)

📊 Data Availability Summary

🔗 Integration with Data Model

BALLOT_MEASURE Entity

🚀 Implementation Roadmap

Phase 1: Ballotpedia Integration (Current Priority)

Phase 2: OpenElections Integration

Phase 3: MIT Election Lab Integration

Phase 4: Real-time Monitoring

📚 References & Credits

Official Sources

Open Civic Data Standards

Related Documentation

🤝 Contributing